When the “rebuilding” San Diego Padres started 2010 well, most thought they wouldn’t stick. However, with with less than fifty games to go, the Padres are still in first place in the National League West. Predictably, various explanations have been given for this, and talk of how they are “staying within themselves” and being “consistent” is cropping up, as in this recent entry by Buster Olney (Insider) quoting a scout to the effect that the Padres don’t have a very good offense outside of Adrian Gonzalez, but are winning more due to their willingness to move guys over and play their “roles” in an intelligent way to maximize their plate appearances.

It is probably true that the Padres are outplaying their “true talent” to an extent, but teams and individuals overperform and underperform their true talent all the time. What is more interesting is the implication that the Padres are getting “more bang for their buck” offensively by doing the “little things” that just help a team win. My interest is not in taking Olney or the scout he quoted to task. Rather, I want to see if the numbers bear out the idea that the Padres are getting more wins out of their offense than they “should” because of their execution, because of the “little things.”

The “little things” are often brought up in reference to teams who outperform their run differential, e.g., some recent Angels teams. The first thing to note about the Padres, however, is that they are not outplaying their Pythagorean expectation: they are actually two wins under what their run differential suggests. So one could argue on that basis alone that the Padres are being “inefficent” in their wins.

But that does not specifically address whether their offense has generated more wins than they “should.” This implies that the Padres have a poor offense. At first glance, one would say “yes,” as the Padres’ team wOBA of .311 (43 linear weights runs [a.k.a. wRAA] below average) is the among the worst in baseball. However, that needs to be understood in context. The Padres have one of the most hitter-unfriendly home parks in the major leagues. In addition, runs above/below average is baselined against all of the MLB, and includes pitchers hitting. To get a better picture, let’s use the park-adjusted linear weights runs from the team value pages and compare to the rest of the NL. In this light, we see that the Padres’ offense is actually four runs above average, and the only team in the NL West above average. So the Padres’ offense has been one of the better in the NL, and the picture of a team miraculously scraping out runs with inferior hitters is already a bit distorting.

Still, even if the Padres offense has been good, is it doing things to deliver more wins than than traditional linear weights measures?

One way of trying to quantify this is to measure their traditional “context-free” linear weights (wRAA, Batting Runs, etc.) against the difference in run expectancy based on base-out state, as I discuss for individuals here. In short, we can subtract a team’s traditional linear weights (“Batting”) from their RE24 to see how much run value is added by hitting “to the context.” Doing this for the Padres (35.84 RE24 – 4.2 Batting) gives a “situational” added value of about 36 runs, which is obviously good.

However, if we’re going to emphasize “context” when discussing a situational hitting, shouldn’t we go all the way, and include not just base/out state, but inning and overall game situation? This is what WPA/LI does. For more detailed explanation of the following, click here, but a brief example can illuminate the difference. Take the following situation: tie game, bottom of the ninth inning, bases loaded, two outs. In this situation, wRAA and RE24 consider a walk and a home run to have very different linear weights values, whereas for WPA/LI it has the same, since it adjusts linear weights to game-state contexts. So if we subtract traditional linear weights (converted to a wins scale) from that, we see how many contextual wins they’ve added beyond the average value of events. And when we do this for the 2010 Padres, we get -0.79 wins. In other words, their offense has actually helped their team win fewer games than one would expect by just looking at the events out of context.

The 2010 Padres are a good team. Their pitching (particularly in relief) has been very good, although that praise should be tempered for the same reasons that we should realize that their offense has actually been better than one might think: the park. They also have been excellent in the field. Those are the reasons that should be given for their success this season. I don’t know whether or not the “little things” stat used above represents a repeatable skill, but whatever the case may be, it is not true that the Padres are getting more wins for less offense.

In the course of a discussion at The Book Blog about the Angels’ (of late) recent outperformance of (some) projections, I was reminded of a related yet quite different issue I’d thought about looking into a while back (and then promptly forgot about). The Angels are one of the teams in baseball that are praised for “playing the right way” and “doing the little things.” Whatever people mean by that, one thing we can say is that recently, the Angels have consistently outperformed their Pythagorean Win Expectation. Looking (somewhat arbitrarily) at the last three seasons in which the Angels have won the American League West and comparing their actual record with what we’d expect given their run differential based on PythagenPat.

2007: Actual 94-68, Expected 90-72, difference +4
2008: Actual 100-62, Expected 88-74, difference +12
2009: Actual 97-65, Expected 93-69, difference +4

I should say right now that this post is not saying that I am not claiming either a) that the Angels “just got lucky” and weren’t as good as their record, or b) that they have some “intangible” ability (perhaps from their manager) that has enabled them to outperform their run differential the last three seasons. Both of those are copouts, at least at this point. For now, I’m only going to look at this issue with reference to their offense.

One might say that they’ve been “good in the clutch.” And that is, in fact, true. FanGraphs’ clutch score, which measures whether players outperform not only their peers, but themselves in high leverage situations, has the following win values for the Angels’ hitter from 2007-2009:

2007: 5.19
2008: 7.34
2009: 3.22

These numbers are impressive, but they sort of beg the question. Unlike relievers, hitters don’t “earn” their high leverage playing time — unless you think most of those scores were put up by Angels pinch-hitters picked for their “clutchness.” This seems to say what we already knew — the Angels won more game than their runs scored indicate that they “should have”. Undoubtedly, there are “clutch hits,” but this doesn’t tell us how they did it — just that they did.

One thing that “right way” teams are praised for is situational hitting. FanGraphs has a stat for that: RE24. While FanGraphs’ primary “runs created above average” stat, wRAA, uses the average change in run expectancy given an event irrespective of the base/out situation, RE24 does incorporate base/out state. For wRAA, a home run is a home run whether the bases are empty with none out or loaded with 2 out, while RE24 takes into account the different base/out run expectation. As I discuss here, if we subtract the average linear weight runs (wRAA) from the RE24, we can see how much better the Angels performed in terms of “situational hitting.”

2007: wRAA +7, RE24 30.5, situational +23.5
2008: wRAA -18, RE24 18.7, situational +36.7
2009: wRAA 88, RE24 92.8, situational +4.8

Impressive. However, it actually doesn’t tell us what we want to know. This tells us that we would expect the Angels to have scored more runs than traditional linear weights (wRAA) would suggest, but the Pythagorean expectation is already using their actual runs scored. We want to know why they outperformed their run differential (for now, from the offensive perspective) — not why they scored more than their linear weights suggest, but why they won more than their actual runs suggest.

Enter WPA/LI. While RE24 takes base/out context into account, WPA/LI goes one step further, by taking base/out/inning into account. You can follow the link to read up, but basically, it’s “unleveraged” Win Probability. It sounds like Clutch, but it’s actually WPA without the Clutch/Leverage element. To use an example to differentiate WPA/LI: with two outs in the bottom of the ninth with the bases loaded, for WPA/LI a walk and a home run have the same linear weight, whereas those events would be different for both wRAA and RE24, since they don’t take game state into account. So, if any stat could take into account a player or team adjusting their play to a situtation, this would be it. As I did in my earlier Little Things post for individuals, we can do for teams: convert wRAA to wins (I crudely divide by 10), then subtract that from WPA/LI to get the situational wins above average linear weights.

2007: wWAA +0.7, WPA/LI -1.32, -2.02 Little Things
2008: wWAA -1.8, WPA/LI -1.21, +0.59 Little Things
2009: wWAA +8.8, WPA/LI +6.37, -2.43 Little Things

Now that is just bizarre. With RE24, we saw that the Angels the last three seasons have been very good at maximizing their situational hitting in certain base/out states. But “Little Things” shows the exact opposite in 2007 and 2009. They’re about “even” in 2008, although far short of what RE24 shows, and they’re 2 wins below their traditional linear weights in 2007 and 2009. It’s not just that the Angels’ hittesr aren’t living up to their reputation (according to this measure) of “doing the little things,” it’s the contrast between RE24 and WPA/LI based “little things” that is striking. It’s as if the Angels do a great job of hitting with runners in scoring position when they’re playing in blowouts, but make terrible situational plays (relative to the average run expectancy) in close games. And then if you look at their hitter’s “Clutch” scores from those years… It’s really hard to know what the big picture is.

This post has no conclusion other than to note that the title is ironic. It would be foolhardy to dismiss this all as luck. The Angels have been a very good team no matter how you slice it. And just because we don’t understand “how they do it” at the moment doesn’t mean we can never know. But at the moment, I’m simply struck by the oddity.

Most of us are still recovering from this week’s Big Awards Euphoria, especially from Monday’s announcement of the 2009 Carter-Batista Award winner (I recommend reading that post before this one), which found that Ryan Ludwick was the 2009 player whose RBI total most exaggerated his offensive contribution.

Personally, I feel that the RBI/wRC system is the best way for figuring out how much RBI totals reflect true offensive contribution. But I also understand that some prefer a more “contextual” approach. As I did at greater length in an earlier series, let’s revisit the same ground using one of FanGraphs’ more context-sensitive stats — RE24 (Cf. Part Two of my Driveline Series) — to discover an “Alternate Universe” winner.

RE24 might appeal to those who believe situational hitting is a repeatable skill (I’m currently agnostic on this). The basic difference between RE24 and traditional linear weights (e.g. wRAA) is that it takes base/out state into account. For traditional linear weights, a double with two men on and two outs “counts” the same as a double with none on and no outs. RE24 recognizes that in those situations, the run expectancy both before and after the plate appearance are different. To quote myself:

There are 24 base-out states (hence the “24” in “RE24”): eight different combinations of baserunners (e.g., runner on first, bases empty, runners on second and third, etc.) multiplied by the three out states in which hitter might have that situation (no outs, 1 out, 2 outs). RE24 measure the difference in Run Expectancy from the beginning of the play until the next play.

For our purposes, the application is obvious — RE24 might identify players who were particularly good in situations with high run expectancy, and thus “earned” their RBI more than wRAA lets on.

To convert RE24 to an “absolute” measure like wRC, subtract the wRAA from wRC and add RE24. I call this “24RC“. Divide RBI by 24RC to get the comparison of real (situational) production to RBI. [Note that it’s not quite apples-to-apples, RE24 is park-adjusted, and the RBI are not, although it’s not a big problem.] The players are ranked by RBI/24RC. I’ve also included a number that sort of isolates situational contribution by subtracting wRAA from RE24. I dubbed it “Sitch.” Clever, huh?

Here are the 2009 Alternate Universe Carter-Batista Award leaders (among qualified hitters with at least 90 RBI).

5. David Ortiz, 1.134 RBI/24RC. .340 wOBA, 99 RBI, 6.40 Sitch
4. Alex Rodriguez, 1.138 RBI/24RC. .405 wOBA, 100 RBI, -10.03 Sitch
3. Michael Cuddyer, 1.141 RBI/24RC. .370 wOBA, 94 RBI, -17.48 Sitch
2. Cody Ross, 1.188 RBI/24RC. .342 wOBA, 90 RBI, -3.02 Sitch
1. Jose Lopez, 1.202 RBI/24RC. .325 wOBA, 96 RBI, 3.72 Sitch

Congratulations, Mr. Jose Lopez! You may have been just outdone by Mr. Ludwick on Monday, but here in the alternate universe, You’re the Man. Maybe in that alternate universe you’re on Shaq Vs., too. Kate Hudson works wonders, I wonder what B-list actress Big Papi is dating? Michael Cuddyer is showing that it’s not his Sitch (or defense) that got him resigned, but those awesome RBI. And what can I say about Cody Ross? Seriously, what can I say?

2009 “Trailers”

47. Adrian Gonzalez, .772 RBI/24RC. .402 wOBA, 5.29 Sitch
48. Joe Mauer, .751 RBI/24RC. .438 wOBA, 0.32 Sitch
49. Chase Utley, .727 RBI/24RC. .402 wOBA, 4.14 Sitch

Someone recently asked me what it would take for Chase Utley to win the NL MVP. I said to wait a couple years for Pujols to reach free agency and come home to Kansas City. I guess I didn’t realize how terrible Chase is at maximizing his RBI opportunities.

2007-2009 Leaders and Trailers (qualifed, 250 RBI minimum):

1. Jeff Francoeur, 1.30 RBI/24RC. .313 wOBA, 252 RBI, -17.62 Sitch
2. Bengie Molina, 1.28 RBI/24RC. .317 wOBA, 256 RBI, 23.29 Sitch
3. Robinson Cano, 1.28 RBI/24RC. .346 wOBA, 254 RBI, -53.47 Sitch
4. Garrett Atkins, 1.19 RBI/24RC. .339 wOBA, 258 RBI, -6.31 Sitch
5. Mike Lowell, 1.18 RBI/24RC. .359 wOBA, 268 RBI, -4.79 Sitch
6. Ryan Howard, 1.16 RBI/24RC. .385 wOBA, 423 RBI, 22.80 Sitch
…
43. Lance Berkman, 0.80 RBI/24RC. .397 wOBA, 288 RBI, 25.32 Sitch
44. Albert Pujols, 0.80 RBI/24RC. .440 wOBA, 354 RBI, 15.22 Sitch
45. Hanley Ramirez, 0.72 RBI/24RC. .409 wOBA, 254 RBI, -27.34 Sitch

Note how much the Sitch scores fluctuate on both ends of the rankings and draw your own conclusions. Any list with Frenchy and Bengie on one end and Pujols and Han-Ram on the other speaks for itself. Other than noting Cano’s Sitch issues (!), I’ll leave it to you all to fill in the blanks. Perhaps this spreadsheet with complete rankings will help.